データ駆動型ナビゲーション：概念・モデル・実験的検証
Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation
ANSFLで開発・実験検証された、人間・動物・自律プラットフォーム向けのデータ駆動型ナビゲーションアルゴリズムを多角的にレビューした論文。
著者: Itzik Klein
分類: cs.RO, cs.AI
原文アブストラクト
The purpose of navigation is to determine the position, velocity, and orientation of manned and autonomous platforms, humans, and animals. Obtaining accurate navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-the-art performance, compared to model-based methods. In this paper we review multidisciplinary, data-driven based navigation algorithms developed and experimentally proven at the Autonomous Navigation and Sensor Fusion Lab (ANSFL) including algorithms suitable for human and animal applications, varied autonomous platforms, and multi-purpose navigation and fusion approaches
